研究使用T2WI和表观扩散系数（ADC）图像进行的MRI放射组学分析，在新辅助化疗和放疗（nCRT）前后分别或同时预测局部晚期直肠癌（LARC）患者nCRT后淋巴结状态的表现。材料和方法：回顾性纳入2022年6月至2022年12月期间，83例LARC患者（训练队列n = 57，验证队列n = 26）。从基线和nCRT后MRI的T2WI和ADC图像上提取了所有放射组学特征的VOI。通过从支持向量机中选择的最具预测性的放射组学标记和临床参数组合构建了七种临床-放射组学模型。接受者操作特征曲线（ROC）用于评估模型的性能。最佳模型基于LNM应用于使用Kaplan-Meier分析评估5年无病生存（DFS）。终点是在术后随访期间临床或放射学的局部区域性复发或远处转移。在训练（AUC = 0.895，95％CI：0.838-0.953）和验证队列（AUC = 0.900，95％CI：0.771-1.000）中，临床- deltaADC放射组学联合模型表现出良好的预测nCRT后LNM的性能。临床-deltaADC放射组学- postT2WI放射组学联合模型也显示出良好的表现（训练集AUC = 0.913，95％CI：0.838-0.953；验证集AUC = 0.912，95％CI：0.771-1.000）。至于亚组分析，临床-deltaADC放射组学联合模型在预测ypT0-T2（AUC = 0.827，95％CI：0.649 ）和ypT3-T4阶段（AUC = 0.934，95％CI：0.864-1.000）的LNM时表现出良好的性能。在ypT0-T2阶段，基于临床-deltaADC放射组学联合模型的LNM可以评估5年DFS（P = 0.030）。临床-deltaADC放射组学联合模型可预测nCRT后LNM，并且基于此联合模型LNM在ypT0-T2阶段与5年DFS有关。© 2023年，作者（以独家许可证授予Springer Science+Business Media，LLC，属于Springer Nature）。

To investigative the performance of MRI-radiomics analysis derived from T2WI and apparent diffusion coefficients (ADC) images before and after neoadjuvant chemoradiation therapy (nCRT) separately or simultaneously for predicting post-nCRT lymph node status in patients with locally advanced rectal cancer (LARC). MATERIALS AND METHODS: Eighty-three patients (training cohort, n = 57; validation cohort, n = 26) with LARC between June 2017 and December 2022 were retrospectively enrolled. All the radiomics features were extracted from volume of interest on T2WI and ADC images from baseline and post-nCRT MRI. Delta-radiomics features were defined as the difference between radiomics features before and after nCRT. Seven clinical-radiomics models were constructed by combining the most predictive radiomics signatures and clinical parameters selected from support vector machine. Receiver operating characteristic curve (ROC) was used to evaluate the performance of models. The optimum model-based LNM was applied to assess 5-years disease-free survival (DFS) using Kaplan-Meier analysis. The end point was clinical or radiological locoregional recurrence or distant metastasis during postoperative follow-up.Clinical-deltaADC radiomics combined model presented good performance for predicting post-CRT LNM in the training (AUC = 0.895,95%CI:0.838-0.953) and validation cohort (AUC = 0.900,95%CI:0.771-1.000). Clinical-deltaADC radiomics-postT2WI radiomics combined model also showed good performances (AUC = 0.913,95%CI:0.838-0.953) in the training and (AUC = 0.912,95%CI:0.771-1.000) validation cohort. As for subgroup analysis, clinical-deltaADC radiomics combined model showed good performance predicting LNM in ypT0-T2 (AUC = 0.827;95%CI:0.649-1.000) and ypT3-T4 stage (AUC = 0.934;95%CI:0.864-1.000). In ypT0-T2 stage, clinical-deltaADC radiomics combined model-based LNM could assess 5-years DFS (P = 0.030).Clinical-deltaADC radiomics combined model could predict post-nCRT LNM, and this combined model-based LNM was associated with 5-years DFS in ypT0-T2 stage.© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.